1,234 research outputs found
Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts
Smart Grid has rapidly transformed the centrally controlled power system into
a massively interconnected cyber-physical system that benefits from the
revolutions happening in the communications (e.g. 5G) and the growing
proliferation of the Internet of Things devices (such as smart metres and
intelligent electronic devices). While the convergence of a significant number
of cyber-physical elements has enabled the Smart Grid to be far more efficient
and competitive in addressing the growing global energy challenges, it has also
introduced a large number of vulnerabilities culminating in violations of data
availability, integrity, and confidentiality. Recently, false data injection
(FDI) has become one of the most critical cyberattacks, and appears to be a
focal point of interest for both research and industry. To this end, this paper
presents a comprehensive review in the recent advances of the FDI attacks, with
particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts
in the Smart Grid infrastructure. This review paper aims to provide a thorough
understanding of the incumbent threats affecting the entire spectrum of the
Smart Grid. Related literature are analysed and compared in terms of their
theoretical and practical implications to the Smart Grid cybersecurity. In
conclusion, a range of technical limitations of existing false data attack
research is identified, and a number of future research directions is
recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl
Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids
This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. Firstly, methods of constructing sparse stealth attacks are developed for two typical scenarios: random attacks in which arbitrary measurements can be compromised and targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method but without the combinatorial complexity. Thirdly, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements as well as attacks even when only
partial observations are collected. The simulations are conducted based on IEEE test systems
Security Aspects of Internet of Things aided Smart Grids: a Bibliometric Survey
The integration of sensors and communication technology in power systems,
known as the smart grid, is an emerging topic in science and technology. One of
the critical issues in the smart grid is its increased vulnerability to cyber
threats. As such, various types of threats and defense mechanisms are proposed
in literature. This paper offers a bibliometric survey of research papers
focused on the security aspects of Internet of Things (IoT) aided smart grids.
To the best of the authors' knowledge, this is the very first bibliometric
survey paper in this specific field. A bibliometric analysis of all journal
articles is performed and the findings are sorted by dates, authorship, and key
concepts. Furthermore, this paper also summarizes the types of cyber threats
facing the smart grid, the various security mechanisms proposed in literature,
as well as the research gaps in the field of smart grid security.Comment: The paper is published in Elsevier's Internet of Things journal. 25
pages + 20 pages of reference
Electric Power Grid Resilience to Cyber Adversaries: State of the Art
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The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of powergeneratio n, advanced monitoring and control systems, and a myriad of emerging modern physical hardware
technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on
detection techniques, protection plans, and mitigation practices in all electricity generation, transmission,
and distribution sectors. This survey discusses such major directions and recent advancements from a lens
of different detection techniques, equipment protection plans, and mitigation strategies to enhance the
energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking
is essential since even modest improvements in resilience of the power grid against cyber threats could lead
to sizeable monetary savings and an enriched overall social welfare
State of the art of cyber-physical systems security: An automatic control perspective
Cyber-physical systems are integrations of computation, networking, and physical processes. Due to the tight cyber-physical coupling and to the potentially disrupting consequences of failures, security here is one of the primary concerns. Our systematic mapping study sheds light on how security is actually addressed when dealing with cyber-physical systems from an automatic control perspective. The provided map of 138 selected studies is defined empirically and is based on, for instance, application fields, various system components, related algorithms and models, attacks characteristics and defense strategies. It presents a powerful comparison framework for existing and future research on this hot topic, important for both industry and academia
False Data Injection Attacks in Smart Grids: State of the Art and Way Forward
In the recent years cyberattacks to smart grids are becoming more frequent
Among the many malicious activities that can be launched against smart grids
False Data Injection FDI attacks have raised significant concerns from both
academia and industry FDI attacks can affect the internal state estimation
processcritical for smart grid monitoring and controlthus being able to bypass
conventional Bad Data Detection BDD methods Hence prompt detection and precise
localization of FDI attacks is becomming of paramount importance to ensure
smart grids security and safety Several papers recently started to study and
analyze this topic from different perspectives and address existing challenges
Datadriven techniques and mathematical modelings are the major ingredients of
the proposed approaches The primary objective of this work is to provide a
systematic review and insights into FDI attacks joint detection and
localization approaches considering that other surveys mainly concentrated on
the detection aspects without detailed coverage of localization aspects For
this purpose we select and inspect more than forty major research contributions
while conducting a detailed analysis of their methodology and objectives in
relation to the FDI attacks detection and localization We provide our key
findings of the identified papers according to different criteria such as
employed FDI attacks localization techniques utilized evaluation scenarios
investigated FDI attack types application scenarios adopted methodologies and
the use of additional data Finally we discuss open issues and future research
direction
Protection Against Graph-Based False Data Injection Attacks on Power Systems
Graph signal processing (GSP) has emerged as a powerful tool for practical
network applications, including power system monitoring. By representing power
system voltages as smooth graph signals, recent research has focused on
developing GSP-based methods for state estimation, attack detection, and
topology identification. Included, efficient methods have been developed for
detecting false data injection (FDI) attacks, which until now were perceived as
non-smooth with respect to the graph Laplacian matrix. Consequently, these
methods may not be effective against smooth FDI attacks. In this paper, we
propose a graph FDI (GFDI) attack that minimizes the Laplacian-based graph
total variation (TV) under practical constraints. In addition, we develop a
low-complexity algorithm that solves the non-convex GDFI attack optimization
problem using ell_1-norm relaxation, the projected gradient descent (PGD)
algorithm, and the alternating direction method of multipliers (ADMM). We then
propose a protection scheme that identifies the minimal set of measurements
necessary to constrain the GFDI output to high graph TV, thereby enabling its
detection by existing GSP-based detectors. Our numerical simulations on the
IEEE-57 bus test case reveal the potential threat posed by well-designed
GSP-based FDI attacks. Moreover, we demonstrate that integrating the proposed
protection design with GSP-based detection can lead to significant hardware
cost savings compared to previous designs of protection methods against FDI
attacks.Comment: This work has been submitted to the IEEE for possible publication.
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